Instructions to use lmms-lab-encoder/onevision-encoder-large-lang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmms-lab-encoder/onevision-encoder-large-lang with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="lmms-lab-encoder/onevision-encoder-large-lang", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lmms-lab-encoder/onevision-encoder-large-lang", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Load model and preprocessor
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model = AutoModel.from_pretrained(
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"lmms-lab-encoder/onevision-encoder-large",
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trust_remote_code=True,
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attn_implementation="flash_attention_2"
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).to("cuda").eval()
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preprocessor = AutoImageProcessor.from_pretrained(
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"lmms-lab-encoder/onevision-encoder-large",
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trust_remote_code=True
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)
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# Load model and preprocessor
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model = AutoModel.from_pretrained(
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"lmms-lab-encoder/onevision-encoder-large-lang",
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trust_remote_code=True,
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attn_implementation="flash_attention_2"
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).to("cuda").eval()
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preprocessor = AutoImageProcessor.from_pretrained(
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"lmms-lab-encoder/onevision-encoder-large-lang",
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trust_remote_code=True
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)
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